Philip O'Neill: Research Interests

Statistical inference for infectious disease data

I am interested in the development of new methodology for analysing data from outbreaks of communicable diseases, and also applying the methods to data from studies. Much of my interest on the methodology side has been around Bayesian Markov chain Monte Carlo (MCMC) methods, and in particular data augmentation techniques. Application areas have so far included influenza, norovirus, measles, E. Coli and some other pathogens.

Healthcare associated infections

Some of the above methods have recently been fruitfully applied to data taken from detailed hospital studies concerned with nosocomial pathogens such as Methicillin Resistant Staphylococcus Aureus (MRSA). In particular this approach is more powerful than that provided by conventional statistical methods both in terms of what is actually assumed, and what can be estimated.

Stochastic epidemic models

I am interested in analysis of stochastic epidemic models, in particular branching process approximations, coupling methods, and quasistationarity.

Stochastic modelling

Other areas of general interest include population modelling, mathematical biology (e.g. cell-level modelling), and quasistationarity of Markov Processes.

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